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[inference] Add a TGI adapter (#52)
* TGI adapter and some refactoring of other inference adapters * Use the lower-level `generate_stream()` method for correct tool calling --------- Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
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Ashwin Bharambe
GitHub
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3 changed files with 256 additions and 0 deletions
15
llama_toolchain/inference/adapters/tgi/__init__.py
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llama_toolchain/inference/adapters/tgi/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_toolchain.core.datatypes import RemoteProviderConfig
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async def get_adapter_impl(config: RemoteProviderConfig, _deps):
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from .tgi import TGIInferenceAdapter
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impl = TGIInferenceAdapter(config.url)
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await impl.initialize()
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return impl
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233
llama_toolchain/inference/adapters/tgi/tgi.py
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llama_toolchain/inference/adapters/tgi/tgi.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import AsyncGenerator, List
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import httpx
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import Message, StopReason
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from llama_models.llama3.api.tokenizer import Tokenizer
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from text_generation import Client
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from llama_toolchain.inference.api import * # noqa: F403
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from llama_toolchain.inference.prepare_messages import prepare_messages
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SUPPORTED_MODELS = {
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"Meta-Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct",
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"Meta-Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
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"Meta-Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct",
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}
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class TGIInferenceAdapter(Inference):
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def __init__(self, url: str) -> None:
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self.url = url.rstrip("/")
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self.tokenizer = Tokenizer.get_instance()
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self.formatter = ChatFormat(self.tokenizer)
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self.model = None
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self.max_tokens = None
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async def initialize(self) -> None:
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hf_models = {v: k for k, v in SUPPORTED_MODELS.items()}
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try:
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print(f"Connecting to TGI server at: {self.url}")
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async with httpx.AsyncClient() as client:
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response = await client.get(f"{self.url}/info")
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response.raise_for_status()
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info = response.json()
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if "model_id" not in info:
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raise RuntimeError("Missing model_id in model info")
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if "max_total_tokens" not in info:
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raise RuntimeError("Missing max_total_tokens in model info")
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self.max_tokens = info["max_total_tokens"]
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model_id = info["model_id"]
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if model_id not in hf_models:
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raise RuntimeError(
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f"TGI is serving model: {model_id}, use one of the supported models: {','.join(hf_models.keys())}"
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)
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self.model = hf_models[model_id]
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except Exception as e:
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import traceback
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traceback.print_exc()
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raise RuntimeError("Could not connect to TGI server") from e
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async def shutdown(self) -> None:
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pass
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async def completion(self, request: CompletionRequest) -> AsyncGenerator:
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raise NotImplementedError()
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def _convert_messages(self, messages: List[Message]) -> List[Message]:
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ret = []
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for message in messages:
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if message.role == "ipython":
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role = "tool"
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else:
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role = message.role
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ret.append({"role": role, "content": message.content})
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return ret
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def get_chat_options(self, request: ChatCompletionRequest) -> dict:
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options = {}
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if request.sampling_params is not None:
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for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
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if getattr(request.sampling_params, attr):
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options[attr] = getattr(request.sampling_params, attr)
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return options
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async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
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messages = prepare_messages(request)
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model_input = self.formatter.encode_dialog_prompt(messages)
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prompt = self.tokenizer.decode(model_input.tokens)
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max_new_tokens = min(
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request.sampling_params.max_tokens or self.max_tokens,
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self.max_tokens - len(model_input.tokens) - 1,
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)
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if request.model != self.model:
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raise ValueError(
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f"Model mismatch, expected: {self.model}, got: {request.model}"
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)
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options = self.get_chat_options(request)
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client = Client(base_url=self.url)
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if not request.stream:
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r = client.generate(
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prompt,
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max_new_tokens=max_new_tokens,
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stop_sequences=["<|eom_id|>", "<|eot_id|>"],
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**options,
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)
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if r.details.finish_reason:
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if r.details.finish_reason == "stop":
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stop_reason = StopReason.end_of_turn
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elif r.details.finish_reason == "length":
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stop_reason = StopReason.out_of_tokens
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else:
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stop_reason = StopReason.end_of_message
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else:
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stop_reason = StopReason.out_of_tokens
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completion_message = self.formatter.decode_assistant_message_from_content(
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r.generated_text, stop_reason
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)
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yield ChatCompletionResponse(
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completion_message=completion_message,
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logprobs=None,
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)
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else:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.start,
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delta="",
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)
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)
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buffer = ""
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ipython = False
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stop_reason = None
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tokens = []
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for response in client.generate_stream(
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prompt,
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max_new_tokens=max_new_tokens,
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stop_sequences=["<|eom_id|>", "<|eot_id|>"],
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**options,
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):
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token_result = response.token
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buffer += token_result.text
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tokens.append(token_result.id)
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if not ipython and buffer.startswith("<|python_tag|>"):
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ipython = True
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.started,
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),
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)
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)
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buffer = buffer[len("<|python_tag|>") :]
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continue
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if token_result.text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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text = ""
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elif token_result.text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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text = ""
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else:
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text = token_result.text
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if ipython:
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delta = ToolCallDelta(
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content=text,
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parse_status=ToolCallParseStatus.in_progress,
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)
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else:
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delta = text
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if stop_reason is None:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=delta,
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stop_reason=stop_reason,
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)
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)
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if stop_reason is None:
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stop_reason = StopReason.out_of_tokens
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# parse tool calls and report errors
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message = self.formatter.decode_assistant_message(tokens, stop_reason)
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parsed_tool_calls = len(message.tool_calls) > 0
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if ipython and not parsed_tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.failure,
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),
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stop_reason=stop_reason,
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)
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)
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for tool_call in message.tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content=tool_call,
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parse_status=ToolCallParseStatus.success,
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),
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stop_reason=stop_reason,
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)
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.complete,
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delta="",
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stop_reason=stop_reason,
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)
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)
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@ -35,6 +35,14 @@ def available_inference_providers() -> List[ProviderSpec]:
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module="llama_toolchain.inference.adapters.ollama",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_id="tgi",
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pip_packages=["text-generation"],
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module="llama_toolchain.inference.adapters.tgi",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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